Felippe Schmoeller da Roza

Deep Learning has increased interest in using artificial intelligence in more and more systems. Reinforcement Learning in Machine Learning has also experienced an upswing. However, despite these impressive recent successes, it still faces the challenge of being accepted and successfully deployed. This is especially true for safety-critical tasks such as robotics, autonomous driving, and industrial control. One approach that can help address this problem is Hierarchical Reinforcement Learning.

Reinforcement learning (RL) is one of three main machine learning (ML) paradigms. Unlike the other two, supervised learning and unsupervised learning, in reinforcement learning the learning takes place in direct interaction with the surroundings. A brief introduction follows.

Achieving safety with AI systems requires a comprehensive and collaborative approach, including technical, ethical, and regulatory considerations. A central aspect is designing AI systems that comply with safety requirements defined in constraints.

Deep Neural Networks (DNNs) have proven their potential. This applies above all to their use in the laboratory. Their performance in real-world applications leaves a lot to be desired. One major problem is the estimation of uncertainty. A closer look at methods that meet this challenge.